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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.28

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the genomic-medicine-sweden/nallo analysis pipeline.
        Report generated on 2025-09-11, 16:25 CEST based on data in: /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/work/6f/cbdc896056619bd855e1b703bcf373

        General Statistics

        Showing 6/6 rows and 17/31 columns.
        Sample NameDupsGCAvg lenMedian lenFailedSeqs≥ 1X≥ 5X≥ 10X≥ 30X≥ 50XMedianMean Cov.Min Cov.Max Cov.Mb Total Coverage BasesGenome lengthVarsSNPIndelTs/TvMNPMultiallelicMultiallelic SNP% Phased VariantsAvg bp per BlockNG50Family IDSex / Het RatioCorrect SexSex
        HG002
        2.3%
        50.0%
        17180bp
        16499bp
        20%
        0.0M
        100.0%
        100.0%
        100.0%
        17.0%
        0.0%
        24X
        24.2X
        8.0X
        33.0X
        0.9Mb
        37068
        57%
        6124
        0
        FAM
        0.0
        True
        male
        HG002_deepvariant_snvs
        169
        122
        47
        2.13
        0
        0
        0
        HG003
        0.9%
        50.0%
        15441bp
        15499bp
        20%
        0.0M
        100.0%
        100.0%
        100.0%
        24.0%
        0.0%
        26X
        23.3X
        6.0X
        39.0X
        0.9Mb
        37068
        67%
        10088
        0
        FAM
        0.0
        True
        male
        HG003_deepvariant_snvs
        161
        119
        42
        2.97
        0
        0
        0
        HG004
        1.4%
        47.0%
        17009bp
        16499bp
        20%
        0.0M
        100.0%
        100.0%
        89.0%
        4.0%
        0.0%
        22X
        19.2X
        4.0X
        31.0X
        0.7Mb
        37068
        64%
        14083
        0
        FAM
        1.4
        True
        female
        HG004_deepvariant_snvs
        263
        172
        91
        1.77
        0
        0
        0

        FastQC

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        TTCTTCCTTTTGTAGTATTATTGTTACAGATGTAACATCTATTAATCCTG
        1
        4
        0.1173%
        CCCACCCCAACCAGCCTCCTACATGTCCACACAGACACCCACCCCAACCA
        1
        4
        0.1173%
        CACGTCCACACAGACACCCCAAACCAGCCTCCTACACGTCCACACAGACA
        1
        4
        0.1173%
        GGCATTCTTGTAGGTTGTGAGAATCACAGGCAGCAGGGCGGGTGCCAGTG
        1
        3
        0.0880%
        AGCATGACAAATAGAATAGTAGCTCATATCTCAATAGTAATGTTGAATGT
        1
        2
        0.0587%
        AGCAAAAGTAGTACTAAGAGAAAAGTTCACAGTATTATTACATTAAGAAT
        1
        2
        0.0587%
        GAGAGAATTTTACAGTGTTATCAAAATGATGAGTGTTTTTCCAAATTTCC
        1
        2
        0.0587%
        TACAAACTTCTGGAAACATTGTTTCCAGACCTTTCCTCTCGTTCTTCTCA
        1
        2
        0.0587%
        ATCGACAGTTGTATCTCTCATCCACCAACATACAGACTAATTTATCAACC
        1
        2
        0.0587%
        ATAACTCCTCATTTCTGAGCACTTGGACAGAGAATGTGCTTACGTGCCTT
        1
        4
        0.1173%
        AGCTGTTGGCCTATCTCTGCTTTCGATTGTCTTTTTCAACCAGCTTAATC
        1
        3
        0.0880%
        CAAACAAACAAAAAACCCAACTAATGACAATAAAATAAACCCTCCCTCAC
        1
        2
        0.0587%
        AACGAAATAAAAGAGGACGCAAACAAATGGCCATACTGCCCAAGGTAATT
        1
        2
        0.0587%
        CTTGTAAAAGGAGCTCTAAATCTTGAATCAAACCCTTGACATACACCAAA
        1
        2
        0.0587%
        AAGTCGCCCCAAATTCTGTCAGAGCAGCCACTGTTTACACTTTGGTGAAG
        1
        2
        0.0587%
        ACCTCAAGTTATCTGCCTGCCTCAGCCCCCAAAGCGTTGAGATGACAGGC
        1
        2
        0.0587%
        AGTCAGTCCCCCAGAGCCACCATGTGGGCATTCTTGTAGGTTGTGAGAAT
        1
        4
        0.1173%
        TGCCCGGGCCCAGAGACTCGGGGGTCCTGCGAGTGCCAATGGCCACACCA
        1
        3
        0.0880%
        TTCCCCCCACTCCACCCACCCCCGGCAAATAACTCCTCATTTCTGAGCAC
        1
        3
        0.0880%
        TCTGCCTTGTCTTCCCAAAGTGGTTTGATTATAGTCATGAGCCACTGAGC
        1
        2
        0.0587%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.URL: https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 3 samples, a BED file was provided, so the data was calculated across those regions. For 3 samples, it's calculated across the entire genome length. 3 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        Somalier

        Genotype to pedigree correspondence checks from sketches derived from BAM/CRAM or VCF.URL: https://github.com/brentp/somalierDOI: 10.1186/s13073-020-00761-2

        Somalier can be used to find sample swaps or duplicates in cancer projects, where there is often no jointly-called VCF across samples. It is also extremely efficient and so can be used to find all-vs-all relatedness estimates for thousands of samples. It also outputs information on sex, depth, heterozgyosity, and ancestry to be used for general QC.

        Statistics

        Various statistics from the somalier report.

        Showing 3/3 rows and 9/24 columns.
        Sample NamePhenotypeSexFather IDMother IDFamily IDInferred sexHetVarHomRefVarHomAltVarNA sitesMean depthDepth stdSites depthGenot depth stdAllele balanceAllele balance stdAllele balance < 0.2, > 0.8HetVar XHomRefVar XHomAltVar XSites XMean depth XSites YMean depth Y
        HG002
        2.0
        		male-9.0-9.0FAM1.0
        13
        13
        9
        0
        14.7X
        4.1
        14.7X
        4.1X
        0.5
        0.4
        0.00
        0
        18
        16
        34
        14.5X
        0
        0.0X
        HG003
        1.0
        		male0.00.0FAM1.0
        7
        12
        14
        2
        14.6X
        4.2
        14.9X
        4.0X
        0.5
        0.5
        0.00
        0
        17
        17
        34
        14.0X
        0
        0.0X
        HG004
        1.0
        		female0.00.0FAM2.0
        15
        8
        4
        8
        10.6X
        4.3
        12.1X
        3.8X
        0.1
        0.7
        0.00
        9
        12
        12
        33
        20.7X
        0
        0.0X

        Relatedness

        Shared allele rates between sample pairs. Points are coloured by degree of expected relatedness: Unrelated, Sib-sib, Parent-child,

        Created with MultiQC

        Relatedness Heatmap

        Heatmap displaying relatedness of sample pairs.

        Created with MultiQC

        Heterozygosity

        Standard deviation of heterozygous allele balance against mean depth.

        A high standard deviation in allele balance suggests contamination.

        Created with MultiQC

        Sex

        Predicted sex against scaled depth on X

        Higher values of depth, low values suggest male.

        Created with MultiQC

        Bcftools

        Utilities for variant calling and manipulating VCFs and BCFs.URL: https://samtools.github.io/bcftoolsDOI: 10.1093/gigascience/giab008

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        WhatsHap

        Phasing genomic variants using DNA reads (aka read-based phasing, or haplotype assembly).URL: https://whatshap.readthedocs.ioDOI: 10.1101/085050

        Phased Basepairs per Sample

        This plot show the total number of phased base pairs for each sample.

        Created with MultiQC

        WhatsHap statistics

        Showing 3/3 rows and 10/13 columns.
        Sample NameInput VariantsHeterozygous VariantsHeterozygous SNVsUnphased VariantsPhased VariantsPhased SNVsBlocksSingletonsTotal Phased bpAvg Variants per Block% Phased VariantsAvg bp per BlockNG50
        HG002
        166
        83
        61
        36
        47
        33
        3
        0
        18372
        15.7
        57%
        6124
        0
        HG003
        158
        73
        57
        24
        49
        38
        4
        0
        40353
        12.2
        67%
        10088
        0
        HG004
        254
        154
        127
        56
        98
        84
        7
        0
        98578
        14.0
        64%
        14083
        0

        Peddy

        Compares familial-relationships and sexes as reported in a PED file with those inferred from a VCF.URL: https://github.com/brentp/peddyDOI: 10.1016/j.ajhg.2017.01.017

        It samples the VCF at about 25000 sites (plus chrX) to accurately estimate relatedness, IBS0, heterozygosity, sex and ancestry. It uses 2504 thousand genome samples as backgrounds to calibrate the relatedness calculation and to make ancestry predictions. It does this very quickly by sampling, by using C for computationally intensive parts, and parallelization.

        Relatedness

        Shared allele rates between sample pairs. Points are coloured by degree of relatedness: less than 0.25, 0.25 - 0.5, greather than 0.5.

        Created with MultiQC

        Het Check

        Proportion of sites that were heterozygous against median depth.

        A high proportion of heterozygous sites suggests contamination, a low proportion suggests consanguinity.

        See the main peddy documentation for more details about the het_check command.

        Created with MultiQC

        Sex Check

        Predicted sex against heterozygosity ratio

        Higher values of Sex Het Ratio suggests the sample is female, low values suggest male.

        See the main peddy documentation for more details about the het_check command.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        ADD_FOUND_IN_TAGbcftools1.2
        busybox_awk1.36.1
        ADD_MOST_SEVERE_CSQadd_most_severe_consequence1.1
        python3.8.3
        ADD_MOST_SEVERE_PLIadd_most_severe_pli1.1
        python3.8.3
        BCFTOOLS_CONCATbcftools1.21
        BCFTOOLS_INDEXbcftools1.21
        BCFTOOLS_MERGEbcftools1.21
        BCFTOOLS_NORM_MULTISAMPLEbcftools1.21
        BCFTOOLS_NORM_SINGLESAMPLEbcftools1.21
        BCFTOOLS_QUERYbcftools1.21
        BCFTOOLS_REHEADERbcftools1.21
        BCFTOOLS_SORTbcftools1.21
        BCFTOOLS_STATSbcftools1.21
        BCFTOOLS_VIEWbcftools1.21
        BEDTOOLS_MERGEbedtools2.31.1
        BEDTOOLS_SORTbedtools2.31.1
        BEDTOOLS_SPLITbedtools2.31.1
        CAT_FASTQcat9.5
        CLEAN_SNIFFLESclean_sniffles1.0
        CRAMINOcramino0.14.5
        CRAMINO_PHASEDcramino0.14.5
        CREATE_SAMPLES_FILEgawk5.1.0
        CREATE_SAMPLES_HAPLOTYPES_FILEgawk5.1.0
        DEEPVARIANT_RUNDEEPVARIANTdeepvariant1.9.0
        DEEPVARIANT_VCFSTATSREPORTdeepvariant1.8.0
        ECHTVAR_ANNOechtvar0.2.2
        ENSEMBLVEP_SNVensemblvep110.0
        tabix1.17
        ENSEMBLVEP_SVensemblvep110.0
        tabix1.17
        FastQCfastqc0.12.1
        GENMOD_ANNOTATEgenmod3.10.1
        GENMOD_COMPOUNDgenmod3.10.1
        GENMOD_MODELSgenmod3.10.1
        GENMOD_SCOREgenmod3.10.1
        GFASTATSgfastats1.3.10
        GLNEXUSglnexus1.4.3-0-gcecf42e
        GUNZIP_FASTAgunzip1.13
        HIFIASMhifiasm0.25.0-r726
        HIFICNVhificnv1.0.0-36e6461
        HIPHASEhiphase1.4.0-7bf2743
        MERGE_JSONmerge_json_sample_files_into_family1.0
        python3.8.3
        MINIMAP2_ALIGNminimap22.29-r1283
        samtools1.21
        MINIMAP2_INDEXminimap22.29-r1283
        MODKIT_BEDMETHYLTOBIGWIGmodkit0.5.0
        MODKIT_PILEUPmodkit0.3.0
        Mosdepthmosdepth0.3.10
        PARAPHASEminimap22.28-r1209
        paraphase3.2.1
        samtools1.21
        Peddypeddy0.4.8
        RELATE_RELATEsomalier0.2.18
        SAMPLESHEET_PEDcreate_pedigree_file1.0
        python3.8.3
        SAMTOOLS_FAIDXsamtools1.21
        SAMTOOLS_FASTQsamtools1.21
        SAMTOOLS_INDEXsamtools1.21
        SAMTOOLS_MERGEsamtools1.21
        SAMTOOLS_SORTsamtools1.21
        SAMTOOLS_VIEWsamtools1.21
        SEVERUSseverus1.5
        SNIFFLESsniffles1.0.12
        SOMALIER_EXTRACTsomalier0.2.18
        SOMALIER_PED_FAMILYcreate_pedigree_file1.0
        python3.8.3
        SPLITUBAMsplitubam0.1.1
        STRANGERstranger0.9.5
        tabix1.22
        SVDB_MERGE_BY_CALLERbcftools1.21
        svdb2.8.2
        SVDB_MERGE_BY_FAMILYbcftools1.21
        svdb2.8.2
        SVDB_QUERYsvdb2.8.2
        TABIX_BGZIPTABIXtabix1.21
        TABIX_ENSEMBLVEP_SNVtabix1.21
        TABIX_ENSEMBLVEP_SVtabix1.21
        TABIX_HIFICNVtabix1.21
        TABIX_SEVERUStabix1.21
        TAGBAMtagbam0.1.0
        TRGT_GENOTYPEtrgt3.0.0
        TRGT_MERGEtrgt3.0.0
        UNTAR_VEP_CACHEuntar1.34
        WHATSHAP_STATSbgzip1.2
        tabix1.2
        whatshap2.3
        WorkflowNextflow25.04.5
        genomic-medicine-sweden/nallov0.8.0dev
        YAK_COUNTyak0.1-r56

        genomic-medicine-sweden/nallo Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/genomic-medicine-sweden/nallo

        Methods

        Data was processed using genomic-medicine-sweden/nallo v0.8.0dev (doi: 10.5281/zenodo.13748210) which uses uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT licence (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.5 (Di Tommaso et al., 2017) with the following command:

        nextflow -quiet -log /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/meta/nextflow.log run /home/xschmy/projects/nallo/tests/../main.nf -c /home/xschmy/projects/nallo/nextflow.config -c /home/xschmy/projects/nallo/tests/nextflow.config -params-file /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/meta/params.json -ansi-log false -profile test,singularity -with-trace /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/meta/trace.csv -w /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/work
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        Tools used in the workflow included: BCFtools (Danecek et al. 2021), BEDTools (Quinlan & Hall 2010), BusyBox's awk, DeepVariant (Poplin et al. 2018), Echtvar (Pedersen & de Ridder 2023), FastQC (Andrews 2010), Felix Lenner (2025), GLnexus (Yun et al. 2021), Genmod (Magnusson et al. 2018), Gfastats (Formenti et al. 2022), HiFiCNV, HiPhase (Holt et al. 2024), Hifiasm (Cheng et al. 2021), Minimap2 (Li 2018), MultiQC (Ewels et al. 2016), Paraphase (Chen et al. 2023), Peddy (Pedersen & Quinlan 2017), Python (Van Rossum & Drake Jr 2009), Renevey (2025), SAMtools (Danecek et al. 2021), SVDB (Eisfeldt et al. 2017), Severus (Keskus et al. 2024), Sniffles (Sedlazeck et al. 2018), Somalier (Pedersen et al. 2020), Stranger (Nilsson & Magnusson 2021), TRGT (Dolzhenko et al. 2024), Tabix (Li 2011), VEP (McLaren et al. 2016), WhatsHap (Martin et al. 2016), add_most_severe_consequence (Neethiraj 2022), add_most_severe_pli (Neethiraj 2022), bgzip, cat (GNU coreutils), clean_sniffles (Eisfeldt 2024), cramino (De Coster & Rademakers 2023), create_pedigree_file, gawk, gunzip, modkit, mosdepth (Pedersen & Quinlan 2018), splitubam, untar, yak.

        References

        • Andrews S, (2010) FastQC, URL: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
        • Ayse Keskus, Asher Bryant, Tanveer Ahmad, Byunggil Yoo, Sergey Aganezov, Anton Goretsky, Ataberk Donmez, Lisa A. Lansdon, Isabel Rodriguez, Jimin Park, Yuelin Liu, Xiwen Cui, Joshua Gardner, Brandy McNulty, Samuel Sacco, Jyoti Shetty, Yongmei Zhao, Bao Tran, Giuseppe Narzisi, Adrienne Helland, Daniel E. Cook, Pi-Chuan Chang, Alexey Kolesnikov, Andrew Carroll, Erin K. Molloy, Irina Pushel, Erin Guest, Tomi Pastinen, Kishwar Shafin, Karen H. Miga, Salem Malikic, Chi-Ping Day, Nicolas Robine, Cenk Sahinalp, Michael Dean, Midhat S. Farooqi, Benedict Paten, Mikhail Kolmogorov. Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads. medRxiv 2024.03.22.24304756; doi: https://doi.org/10.1101/2024.03.22.24304756
        • Brent S Pedersen, Jeroen de Ridder, Echtvar: compressed variant representation for rapid annotation and filtering of SNPs and indels, Nucleic Acids Research, Volume 51, Issue 1, 11 January 2023, Page e3, https://doi.org/10.1093/nar/gkac931
        • Cheng, H., Concepcion, G.T., Feng, X. et al. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021). https://doi.org/10.1038/s41592-020-01056-5
        • Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10(2):giab008. doi:10.1093/gigascience/giab008
        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Dolzhenko, E., English, A., Dashnow, H. et al. Characterization and visualization of tandem repeats at genome scale. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02057-3
        • Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PMID: 27312411; PMCID: PMC5039924.
        • Genome-wide profiling of highly similar paralogous genes using HiFi sequencing. Xiao Chen, Daniel Baker, Egor Dolzhenko, Joseph M Devaney, Jessica Noya, April S Berlyoung, Rhonda Brandon, Kathleen S Hruska, Lucas Lochovsky, Paul Kruszka, Scott Newman, Emily Farrow, Isabelle Thiffault, Tomi Pastinen, Dalia Kasperaviciute, Christian Gilissen, Lisenka Vissers, Alexander Hoischen, Seth Berger, Eric Vilain, Emmanuèle Délot, UCI Genomics Research to Elucidate the Genetics of Rare Diseases (UCI GREGoR) Consortium, Michael A Eberle. bioRxiv 2024.04.19.590294; doi: https://doi.org/10.1101/2024.04.19.590294
        • Giulio Formenti, Linelle Abueg, Angelo Brajuka, Nadolina Brajuka, Cristóbal Gallardo-Alba, Alice Giani, Olivier Fedrigo, Erich D Jarvis, Gfastats: conversion, evaluation and manipulation of genome sequences using assembly graphs, Bioinformatics, Volume 38, Issue 17, September 2022, Pages 4214–4216, https://doi.org/10.1093/bioinformatics/btac460
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • Heng Li, Minimap2: pairwise alignment for nucleotide sequences, Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094–3100, https://doi.org/10.1093/bioinformatics/bty191
        • James M Holt, Christopher T Saunders, William J Rowell, Zev Kronenberg, Aaron M Wenger, Michael Eberle, HiPhase: jointly phasing small, structural, and tandem repeat variants from HiFi sequencing, Bioinformatics, Volume 40, Issue 2, February 2024, btae042, https://doi.org/10.1093/bioinformatics/btae042
        • Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27(5):718-719. doi:10.1093/bioinformatics/btq671
        • Magnusson M, Hughes T, Glabilloy, Bitdeli Chef. genmod: Version 3.7.3. Published online November 15, 2018. doi:10.5281/ZENODO.3841142
        • Marcel Martin, Murray Patterson, Shilpa Garg, Sarah O Fischer, Nadia Pisanti, Gunnar W Klau, Alexander Schöenhuth, Tobias Marschall. bioRxiv 085050; doi: https://doi.org/10.1101/085050
        • McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1):122. doi:10.1186/s13059-016-0974-4
        • Nilsson D, Magnusson M. moonso/stranger v0.7.1. Published online February 18, 2021. doi:10.5281/ZENODO.4548873
        • Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Hancock J, ed. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699
        • Pedersen BS, Quinlan AR. Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy. The American Journal of Human Genetics, Volume 100, Issue 3, March 2017, Pages 406-413, http://dx.doi.org/10.1016/j.ajhg.2017.01.017
        • Pedersen, B.S., Bhetariya, P.J., Brown, J. et al. Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches. Genome Med 12, 62 (2020). https://doi.org/10.1186/s13073-020-00761-2
        • Poplin R, Chang PC, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-987. doi:10.1038/nbt.4235
        • Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26, 6, pp. 841–842.
        • Sedlazeck, F.J., Rescheneder, P., Smolka, M. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods 15, 461–468 (2018). https://doi.org/10.1038/s41592-018-0001-7
        • Wouter De Coster, Rosa Rademakers, NanoPack2: population-scale evaluation of long-read sequencing data, Bioinformatics, Volume 39, Issue 5, May 2023, btad311, https://doi.org/10.1093/bioinformatics/btad311
        • Yun T, Li H, Chang PC, Lin MF, Carroll A, McLean CY. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Robinson P, ed. Bioinformatics. 2021;36(24):5582-5589. doi:10.1093/bioinformatics/btaa1081
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

        genomic-medicine-sweden/nallo Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/genomic-medicine-sweden/nallo

        Input/output options

        echtvar_snv_databases
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/testdata/snp_dbs.csv
        genmod_reduced_penetrance
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/reduced_penetrance.tsv
        genmod_score_config_snvs
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/rank_model_snv.ini
        genmod_score_config_svs
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/rank_model_svs.ini
        hificnv_excluded_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/empty.bed
        hificnv_expected_xx_cn
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/expected_cn.hg38.XX.bed
        hificnv_expected_xy_cn
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/expected_cn.hg38.XY.bed
        input
        /home/xschmy/projects/nallo/assets/samplesheet_multisample_bam.csv
        methylation_call_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/test_data.bed
        outdir
        /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/output
        par_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/hs38.PAR.bed
        peddy_sites
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/peddy.sites
        qc_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/test_data.bed
        snv_call_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/test_data.bed
        somalier_sites
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/somalier_sites.vcf.gz
        str_bed
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/pathogenic_repeats.hg38.bed
        stranger_repeat_catalog
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/variant_catalog_grch38.json
        sv_call_regions
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/test_data.bed
        svdb_sv_databases
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/testdata/svdb_dbs.csv
        trace_report_suffix
        2025-09-11_16-21-25
        variant_consequences_snvs
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/variant_consequences_v2.txt
        variant_consequences_svs
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/variant_consequences_v2.txt
        vep_cache
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/vep_cache_test_data.tar.gz

        Reference genome options

        fasta
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/hg38.test.fa.gz

        Institutional config options

        config_profile_description
        Minimal test dataset to check pipeline function
        config_profile_name
        Test profile

        Workflow options

        alignment_processes
        2
        extra_hifiasm_options
        -f0 -k30 -w30 -D10 -r1 -N1
        extra_modkit_options
        --seed 1 --sampling-frac 0.1
        extra_paraphase_options
        --gene hba,OR1D5
        extra_vep_options
        --plugin SpliceAI,snv=spliceai_21_scores_raw_snv_-v1.3-.vcf.gz,indel=spliceai_21_scores_raw_snv_-v1.3-.vcf.gz
        extra_yak_options
        -b0
        phaser
        hiphase
        snv_calling_processes
        2
        sv_callers
        severus,hificnv
        sv_callers_merge_priority
        severus,hificnv
        sv_callers_to_merge
        severus,hificnv
        sv_callers_to_run
        severus,hificnv,sniffles
        vep_plugin_files
        https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/4645adc45ba1ea0363b19ba3ef3c52d62193816f/reference/vep_plugin_files.csv

        Core Nextflow options

        configFiles
        /home/xschmy/.nextflow/config, /home/xschmy/projects/nallo/nextflow.config, /home/xschmy/projects/nallo/nextflow.config, /home/xschmy/projects/nallo/tests/nextflow.config
        containerEngine
        singularity
        launchDir
        /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94
        profile
        test,singularity
        projectDir
        /home/xschmy/projects/nallo
        runName
        zen_woese
        userName
        xschmy
        workDir
        /home/xschmy/projects/nallo/.nf-test/tests/ec52410c7f9883e74f77293152100d94/work

        MultiQC v1.28 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.